Arteriosclerosis (i.e., atherosclerosis and arteriolosclerosis) of the cardiac, cerebral, renal, and peripheral arteries leads to target organ damage and clinical sequelae such as heart attack and failure, stroke and dementia, chronic kidney disease, and claudication. Because of multiple etiologic pathways, variations in a large number of genes are likely to influence susceptibility to target organ damage in hypertensive individuals. The Genetic Epidemiology Network of Arteriopathy (GENOA) was initiated in 1995 to study the genetics of hypertension and its target organ complications using linkage analysis in sibships. As part of the ongoing GENOA study, quantitative measures of Arteriosclerosis including both atherosclerotic/ macrovascular measures of coronary artery calcification, aortic root diameter, ankle-brachial BP index, and arteriolosclerotic /microvascular measures of ischemic brain injury (subcortical white matter hyperintensities, brain atrophy, and ventricular volume) and chronic kidney disease (serum creatinine and albuminuria) have been collected. In this application, we propose to conduct a genome-wide association study of these phenotypic measures of arteriosclerosis, utilizing 500,000 single nucleotide polymorphisms measured on 1418 African-Americans and 1095 non-Hispanic Whites from the GENOA cohort. With the following specific aims: Aim 1: To identify genomic regions containing evidence of disease susceptibility loci for quantitative measures of Arteriosclerosis in 1418 African-American and 1095 non-Hispanic white GENOA participants using 500,000 SNPs. We will perform genome-wide association analysis in these two samples using state-of-the-art univariate and multivariate approaches to identify trait-specific loci, as well as genetic loci with pleiotropic effects on multiple genetically correlated measures of arteriosclerosis. Capitalizing on the hypertensive sibpair structure of our data, we will create two replicate sets of hypertensives (one sib in unrelated Set 1, second sib in unrelated Set 2) to perform replicate genome-wide associations. SNP associations will be adjusted for multiple testing, assessed for their predictive utility using cross-validation, and compared for replicate evidence to reduce false positives. Aim 2: To investigate the role of context dependent genetic effects on the distribution of target organ disease in these cohorts, we will test for effects of SNP-covariate interactions (age, sex, smoking, obesity, alcohol, physical activity, education, blood pressure drug treatment) and SNP-SNP interactions on the same quantitative measures of Arteriosclerosis investigated in Aim 1. SNP associations will be adjusted for multiple testing, assessed for their predictive utility using cross-validation, and compared for replicate evidence to reduce false positives. We will also use supervised pattern recognition methods (classification and regression trees, random forests, genomic identity-by-state methods) to build predictive models of these clinical phenotypes to identify at-risk individuals. (End of Abstract)